Bagging and Random Forests

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Explores decision trees, ensembles, CLT, inference, machine learning, diagnostic methods, boosting, and variance estimation.

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Explores overfitting in decision trees and introduces random forests as a solution.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Explores random forests as a powerful ensemble method for classification, discussing bagging, stacking, boosting, and sampling strategies.

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Explores language detection using Recurrent Neural Networks and supervised learning concepts.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.